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KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

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Iran Journal of computer Science 2023年第4期6卷 307-319页

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Ul. Nowowiejska 15/19 Warsaw 00-665 Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Tehran Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature have somehow mitigated some of the above-mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture initialized with a pre-trained MobileNetV2 model. In addition, to better study the detectors in iris recognition scenarios, we have collected 1000 images. The provided dataset (KartalOl) is made publicly available for the research community. In the proposed framework, to have better generalization, we fine-tuned the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Likewise, data augmentation techniques are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa, along with the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The implementation source code of KartalOl is made publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © The Author(s) 2023.

关键词： Iris segmentation Localization MobileNetV2 model Unconstrained environment

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Near-infrared optical thermometry of nickel color centers in diamond

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Optics Letters 2025年第3期50卷 968-971页

作者： Dharmasiri, A. Vincent, C. Rajib, T.I. Pochechuev, M. Liu, X. Verhoef, A.J. Hemmer, P.R. Zheltikov, A.M. Institute for Quantum Science and Engineering Texas A&M University College StationTX77843 United States Department of Physics and Astronomy Texas A&M University College StationTX77843 United States Department of Soil and Crop Sciences Texas A&M University College StationTX77843 United States Department of Electrical & Computer Engineering Texas A&M University College StationTX77843 United States

We present an experimental study of temperature-dependent near-infrared fluorescence spectra of nickel color centers in diamond. The amplitude, the central wavelength, and the linewidth of the zero-phonon line (ZPL) in the fluorescence spectrum of these centers exhibit a strong temperature dependence, enabling highly sensitive temperature measurements. Due to the ZPL wavelength, falling within the biological transparency window, combined with a high-temperature sensitivity and low noise floor, as demonstrated by our experiments in practical thermometry settings, nickel color centers in diamond are ideally suited for all-optical temperature measurements, including thermometry of biological systems. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Infrared devices

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Cloud-Powered Virtual Doctor Robots for Enhancing Elderly Care and Chronic Disease Management with Recurrent Neural Networks 3

Cloud-Powered Virtual Doctor Robots for Enhancing Elderly Ca...

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3rd International Conference on electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Ramesh, S. Mohankumar, N. Kovarasan, Rajesh Kambattan Selvamanikandan, R. Yuvaraj, S. Sujatha, S. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India Maharashtra Nagpur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Trichy India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369083

This research introduces cloud-powered virtual doctor robots to improve elderly care and chronic illness management. This system uses recurrent neural networks (RNNs) to give personalized medical care to elderly chronic disease patients. These virtual doctor robots may integrate with cloud computing infrastructure to access huge medical databases for real-time patient data analysis and individualized treatment and care suggestions. Healthcare technology has advanced by using RNNs to learn from patient data, adapt to changing health situations, and predict prospective health issues. RNNs can interpret sequential data, thus virtual doctor robots may identify trends in patient health parameters, forecast illness development, and suggest risk mitigation strategies. Cloud-powered virtual doctor robots provide a scalable solution to conventional healthcare systems rising issues. These robots can work effectively in resource-constrained areas by offloading computational chores to distant servers, providing continuous medical expertise and lowering healthcare staff reliance. This technique might improve aged care and reduce load on overworked healthcare systems. Using proactive monitoring, timely intervention, and personalized guidance, virtual doctor robots with RNNs can improve health outcomes, empower patients to manage chronic conditions, and create a more sustainable healthcare model. © 2024 IEEE.

关键词： Medical robotics

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Dynamic AI-Driven Network Slicing with O-RAN for Continuous Connectivity in Connected Vehicles and Onboard Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 720-733页

作者： Shah, Syed Danial Ali Bashir, Ali Kashif Al-Otaibi, Yasser D. Dabel, Maryam M. Al Ali, Farman University of Leeds School of Electronic and Electrical Engineering LeedsLS2 9JT United Kingdom Manchester Metropolitan University Department of Computing and Mathematics M15 6BH United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah21589 Saudi Arabia University of Hafr Al Department of Computer Science and Engineering Batin Saudi Arabia Sungkyunkwan University School of Convergence Seoul Korea Republic of

The rise of connected and autonomous vehicles signifies an era of intelligent transportation systems, where robust and continued network connectivity is essential for critical applications and enhanced in-vehicle Consumer Electronics (CE) experiences. Slicing at the network's edge offers tailored and dedicated logical networks for diverse and low-latency vehicular demands, including Advanced Driver Assistance Systems (ADAS) and in-car infotainment. However, seamless migration of network slices as vehicles traverse coverage areas of different network operators presents formidable challenges, such as ensuring continuous connectivity and uninterrupted service for both safety-critical systems and consumer-oriented services. In this paper, we introduced dynamic network slicing for continuous connectivity in connected vehicles and onboard CE using the Open Radio Access Network (O-RAN) framework in a highly dynamic and mobile environment. We implemented an xAPP within O-RAN that enables Deep Reinforcement Learning (DRL) agent to learn optimal policies through interaction with the network, guiding intelligent decisions on slice migration, resource allocation, and handover optimization. We conducted simulations and evaluations to demonstrate the effectiveness of the proposed xAPP in maintaining optimal Quality of Service (QoS), ensuring efficient RAN resource utilization, minimizing service interruptions, and prioritizing safety-critical slices, all while supporting seamless operation of CE within vehicles during mobility. © 1975-2011 IEEE.

关键词： Advanced driver assistance systems

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Pulsed Homodyne Measurement of Multi-Mode Squeezed Vacua for Large Scale quantum Computation

Pulsed Homodyne Measurement of Multi-Mode Squeezed Vacua for...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Nomura, Takefumi Endo, Mamoru Kashiwazaki, Takahiro Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan NTT Device Technology Labs. NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts-Amherst AmherstMA01003 United States Department of Physics University of Massachusetts-Amherst AmherstMA01003 United States

Optical quantum computers require a large number of squeezed vacua. In this research, 36 squeezed spectral modes were produced with type-0 lithium niobate waveguide, thus demonstrating its scalability as a resource fo... 详细信息

ISBN: (纸本)9781957171395

关键词： quantum computers

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Fundamental Limit on the Power of Entanglement Assistance in quantum Communication

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Physical Review Letters 2025年第2期134卷 020802-020802页

作者： Lasse H. Wolff Paula Belzig Matthias Christandl Bergfinnur Durhuus Marco Tomamichel Department of Mathematical Sciences University of Copenhagen Universitetsparken 5 2100 Denmark Institute for Quantum Computing University of Waterloo 200 University Avenue West Waterloo Ontario N2L3G1 Canada Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 117583 Singapore and Centre for Quantum Technologies 3 Science Drive 2 117543 Singapore

The optimal rate of reliable communication over a quantum channel can be enhanced by preshared entanglement. Whereas the enhancement may be unbounded in infinite-dimensional settings even when the input power is constrained, a long-standing conjecture asserts that the ratio between the entanglement-assisted and unassisted classical capacities is bounded in finite-dimensional settings [Bennett et al., IEEE Trans. Inf. Theory 48, 2637 (2002)]. In this Letter, we prove this conjecture by showing that their ratio is upper bounded by o(d2), where d is the input dimension of the channel. An application to quantum communication with noisy encoders and decoders is given.

关键词： quantum channel

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A Distributed Pricing Mechanism and Optimal Schedulingin Smart Grid using Improved Grey Wolf Optimization

A Distributed Pricing Mechanism and Optimal Schedulingin Sma...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Nagendar, Y. Alabdeli, Haider Prashanth, V. Aarthy, G. Thangavel, C. Sr University School of Computer Science and Engineering Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India Reva University School of Computing and Information Technology Bengaluru India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Mechanical Engineering Salem India

ISBN: (纸本)9798350360660

In recent days, energy is most important resource new techniques and methods which introduced to fulfill energy demand for price-based demand response (DR). The preface to this study has shown that load management strategies implemented based on Costlier scheduling for low or consistent energy users results from price-based demand response strategies. Suggested work has taken into account and broadened the same concept to include numerous EV charging regions and corresponding loads from an analytical and implementation point of view. This research provides an innovative approach called Improved Grey Wolf Optimization (IGWO) that uses the distinct energy consumption patterns of EVs in each location to calculate EV charging costs. Dynamic price signal that is non-discriminatory is sent to all EV regions and stations. First, a brand-new mathematical model based on market dynamics and real-time load demand is provided to determine charging prices. The global optimum solution produced by the optimization algorithm successfully converges, confirming the efficacy of the suggested mechanism. Lastly, analysis and simulation outcomes are performed to demonstrate the accomplishments of our suggested task in standings of reasonable cost high user satisfaction distribution. IGWO demonstrated usefulness's revenue is modest by either mechanism or cost saving is 0.69-6.89%. © 2024 IEEE.

关键词： Demand response

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Automated Fire Hydrant Monitoring and Maintenance using IoT Data Analytics and Naive Bayes Classifier

Automated Fire Hydrant Monitoring and Maintenance using IoT ...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sarasu, R. Nandagopal, V. Shibu, N.V. Lakshminarayanan, Suresh Raj, T Saju Rajmohan, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics Chennai India Mohan Babu University School of Engineering Department of Electrical and Electronics Engineering Tirupathi India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai India University of Technology and Applied Sciences Department of Engineering & Electronics South Al Batina Oman Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350375442

This paper introduces a new method for managing fire hydrants that uses a monitoring system based on the Internet of Things (IoT) and a Naive Bayes Classifier (NBC) for predictive maintenance. Water pressure, flow rate, and the physical state of fire hydrants are among the essential factors for the proposed system continually collecting data using an IoT sensor network. The system analyzes real-time data using NBC. These predictive capabilities reduce the potential of hydrant breakdowns during emergencies through preventive maintenance potential. It introduces a reliable IoT infrastructure for fire hydrant monitoring, improving the acquired data's granularity and timeliness. NBC provides a powerful instrument for early problem detection, significantly improving maintenance effectiveness. Since the technology is compatible with existing fire department procedures, it can easily integrate into current operational processes. Better public safety, more efficient use of resources, and more dependable fire hydrants are potential outcomes of responsive and proactive data-driven maintenance techniques. The findings improve overall fire safety and operational efficiency, with a 25% decrease in maintenance expenditures and a 40% increase in issue detection accuracy. © 2024 IEEE.

关键词： Predictive maintenance

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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Incompatibility as a Resource for Programmable quantum Instruments

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PRX quantum 2024年第1期5卷 010340-010340页

作者： Kaiyuan Ji Eric Chitambar Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing 100084 China School of Electrical and Computer Engineering Cornell University Ithaca New York 14850 USA Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA

quantum instruments represent the most general type of quantum measurement, as they incorporate processes with both classical and quantum outputs. In many scenarios, it may be desirable to have some “on-demand” device that is capable of implementing one of many possible instruments whenever the experimenter desires. We refer to such objects as programmable instrument devices (PIDs), and this paper studies PIDs from a resource-theoretic perspective. A physically important class of PIDs are those that do not require quantum memories to implement, and these are naturally “free” in this resource theory. Additionally, these free objects correspond precisely to the class of unsteerable channel assemblages in the study of channel steering. The traditional notion of measurement incompatibility emerges as a resource in this theory since any PID controlling an incompatible family of instruments requires a quantum memory to build. We identify an incompatibility preorder between PIDs based on whether one can be transformed into another using processes that do not require additional quantum memories. Necessary and sufficient conditions are derived for when such transformations are possible based on how well certain guessing games can be played using a given PID. Ultimately our results provide an operational characterization of incompatibility, and they offer semi-device-independent tests for incompatibility in the most general types of quantum instruments.

关键词： quantum communication, protocols & technology quantum correlations in quantum information quantum correlations, foundations & formalism quantum information processing quantum measurements quantum memories quantum networks Resource theories

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